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The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview
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In: JMIR Med Inform (2020)
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A Comparison of Word Embeddings for the Biomedical Natural Language Processing ...
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Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes ...
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Clinical documentation variations and NLP system portability: a case study in asthma birth cohorts across institutions
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In: J Am Med Inform Assoc (2018)
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Abstract:
OBJECTIVE: To assess clinical documentation variations across health care institutions using different electronic medical record systems and investigate how they affect natural language processing (NLP) system portability. MATERIALS AND METHODS: Birth cohorts from Mayo Clinic and Sanford Children’s Hospital (SCH) were used in this study (n = 298 for each). Documentation variations regarding asthma between the 2 cohorts were examined in various aspects: (1) overall corpus at the word level (ie, lexical variation), (2) topics and asthma-related concepts (ie, semantic variation), and (3) clinical note types (ie, process variation). We compared those statistics and explored NLP system portability for asthma ascertainment in 2 stages: prototype and refinement. RESULTS: There exist notable lexical variations (word-level similarity = 0.669) and process variations (differences in major note types containing asthma-related concepts). However, semantic-level corpora were relatively homogeneous (topic similarity = 0.944, asthma-related concept similarity = 0.971). The NLP system for asthma ascertainment had anF-score of 0.937 at Mayo, and produced 0.813 (prototype) and 0.908 (refinement) when applied at SCH. DISCUSSION: The criteria for asthma ascertainment are largely dependent on asthma-related concepts. Therefore, we believe that semantic similarity is important to estimate NLP system portability. As the Mayo Clinic and SCH corpora were relatively homogeneous at a semantic level, the NLP system, developed at Mayo Clinic, was imported to SCH successfully with proper adjustments to deal with the intrinsic corpus heterogeneity.
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Keyword:
Research and Applications
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URL: https://doi.org/10.1093/jamia/ocx138 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378885/ http://www.ncbi.nlm.nih.gov/pubmed/29202185
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A Comparison of Word Embeddings for the Biomedical Natural Language Processing
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Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation
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In: Health Informatics & Administration Faculty Articles (2018)
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Aligned-Layer Text Search in Clinical Notes
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In: Stud Health Technol Inform (2017)
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BELMiner: adapting a rule-based relation extraction system to extract biological expression language statements from bio-medical literature evidence sentences
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The CHEMDNER corpus of chemicals and drugs and its annotation principles
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DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx
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In: PMC (2015)
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Learning Semantic Tags from Big Data for Clinical Text Representation
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Assessing the Need of Discourse-Level Analysis in Identifying Evidence of Drug-Disease Relations in Scientific Literature
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Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification
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Using Empirically Constructed Lexical Resources for Named Entity Recognition
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Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis
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Towards a semantic lexicon for clinical natural language processing
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Semantic Characteristics of NLP-extracted Concepts in Clinical Notes vs. Biomedical Literature
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